唐山市PM_(2.5)污染对城市居民呼吸系统疾病日门诊量的影响
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摘要
目的评价PM_(2.5)污染程度对唐山市城市居民呼吸系统疾病日门诊量的影响。方法收集唐山市2013年11月-2014年3月、2014年11月-2015年3月、2015年11月-2016年3月的气象监测数据、大气污染物浓度资料及各三级甲等综合性医院呼吸系统疾病日门诊人数,采用Pearson相关分析大气污染物、气象因素以及呼吸系统疾病门诊量之间的相关性;采用时间序列的广义相加模型分析大气污染浓度与呼吸系统疾病日门诊量之间的关联性。结果 2013年11月-2014年3月、2014年11月-2015年3月、2015年11月-2016年3月三个时间段内,各大气污染物之间每日浓度的相关性分析结果显示PM_(10)、PM_(2.5)、SO_2、NO_2、CO之间存在明显的正相关,各大气污染物与气湿之间呈正相关,除PM_(2.5)外,其他污染物与呼吸系统疾病日门诊量之间无明显的相关性。其中PM_(2.5)分别滞后1、2、4 d对呼吸系统疾病日门诊量影响最大。且PM_(2.5)浓度每增加10μg/m~3时,呼吸系统疾病日门诊量分别增加0.25%(95%CI:0.18%～0.32%)、0.65%(95%CI:0.31%～0.99%)、0.42%(95%CI:0.11%～0.73%)。结论唐山市PM_(2.5)污染程度增高会导致呼吸系统疾病日门诊量的增加。
Objective To estimate the impact of PM_(2.5) pollution on the daily outpatient visits for respiratory diseases in urban residents in Tangshan City. Methods We collected meteorological monitoring data, air pollutant concentration data and daily outpatient visits for respiratory diseases from all grade-A tertiary general hospitals in Tangshan City from November 2013 to March 2014, from November 2014 to March 2015 and from November 2015 to March 2016. The correlations among air pollutants, meteorological factors and daily outpatient volume of respiratory diseases were evaluated by Pearson correlation. The associations between air pollutants and daily outpatient volume of respiratory diseases were analyzed by using the generalized additive model(GAM) of time series. Results The correlation analysis on daily concentration of all air pollutants during the above-mentioned three periods showed that there were significantly positive correlations among PM_(10), PM_(2.5), SO_2, NO_2 and CO as well as between each air pollutant and humidity, but there was no significant correlation between all air pollutants except PM_(2.5) and daily outpatient visits for respiratory diseases. PM_(2.5) for 1-day, 2-day and 4-day lag was positively associated with daily outpatient volume of respiratory diseases; moreover, the daily outpatient volume of respiratory diseases increased by 0.25%(95%CI:0.18%-0.32%), 0.65%(95%CI:0.31%-0.99%) and 0.42%(95%CI: 0.11%-0.73%) respectively with PM_(2.5) concentration increase per 10 μg/m~3. Conclusions The increase of atmospheric PM_(2.5) pollution in Tangshan City can lead to the increment of daily outpatient volume of respiratory diseases.
Objective To estimate the impact of PM_(2.5) pollution on the daily outpatient visits for respiratory diseases in urban residents in Tangshan City. Methods We collected meteorological monitoring data, air pollutant concentration data and daily outpatient visits for respiratory diseases from all grade-A tertiary general hospitals in Tangshan City from November 2013 to March 2014, from November 2014 to March 2015 and from November 2015 to March 2016. The correlations among air pollutants, meteorological factors and daily outpatient volume of respiratory diseases were evaluated by Pearson correlation. The associations between air pollutants and daily outpatient volume of respiratory diseases were analyzed by using the generalized additive model(GAM) of time series. Results The correlation analysis on daily concentration of all air pollutants during the above-mentioned three periods showed that there were significantly positive correlations among PM_(10), PM_(2.5), SO_2, NO_2 and CO as well as between each air pollutant and humidity, but there was no significant correlation between all air pollutants except PM_(2.5) and daily outpatient visits for respiratory diseases. PM_(2.5) for 1-day, 2-day and 4-day lag was positively associated with daily outpatient volume of respiratory diseases; moreover, the daily outpatient volume of respiratory diseases increased by 0.25%(95%CI:0.18%-0.32%), 0.65%(95%CI:0.31%-0.99%) and 0.42%(95%CI: 0.11%-0.73%) respectively with PM_(2.5) concentration increase per 10 μg/m~3. Conclusions The increase of atmospheric PM_(2.5) pollution in Tangshan City can lead to the increment of daily outpatient volume of respiratory diseases.
引文
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[16] 康真,刘洋,于天一,等.2015-2016年哈尔滨市大气PM2.5污染与儿童门诊量关系的时间序列研究[J].中国卫生工程学,2018,17(4):481-484.
[2] Touloumi G,Samoli E,Quenel P,et al.Short-term effects of air pollution on total and cardiovascular mortality:the confounding effect of influenza epidemics[J].Epidemiology,2005,16(1):49-57.
[3] Rodopoulou S,Chalbot MC,Samoli EA,et al.Air pollution and hospital emergency room and admissions for cardiovascular and respiratory diseases in Doria Ana County,New Mexico[J].Environ Res,2014,12(9):39-46.
[4] Brook RD,Rajagopalan S,Pope CA 3rd,et al.Particulate matter air pollution and cardiovascular disease:an update to the scientific statement from the American Heart Association[J].Circulation,2010,121(21):2331-2378.
[5] Qiu H,Tian LW,Pun VC,et al.Coarse particulate matter associated with increased risk of emergency hospital admissions for pneumonia in Hong Kong[J].Thorax,2014,69(11):1027-1033.
[6] Wang Y,Zu Y,Huang L,et al.Associations between daily outpatient visits for respiratory diseases and ambient fine particulate matter and ozone levels in Shanghai,China[J].J Expo Sci Environ Epidemiol,2018,240(2018):754-763.
[7] Wang JF,Yin Q,Tong SL,et al.Prolonged continuous exposure to high fine particulate matter associated with cardiovascular and respiratory disease mortality in Beijing,China[J].Atmos Environ,2017,168(2017):1-7.
[8] 王荀,廖玉学,刘丽红,等.深圳市龙岗区大气污染物与医院呼吸系统疾病门诊量的广义相加模型分析[J].实用预防医学,2019,26(1):59-62.
[9] 姜彩霞,朱冰,张龙.2013-2014年杭州市大气PM2.5与呼吸系统疾病就诊人次的时间序列研究[J].环境与职业医学,2018,35(7):589-595.
[10] 段振华,高绪芳,杜慧兰,等.成都市空气PM2.5浓度与呼吸系统疾病门诊人次的时间序列研究[J].现代预防医学,2015,42(4):611-614.
[11] 张文增,甄国新,陈东妮,等.北京市顺义区大气污染物对呼吸系统疾病门诊量的影响[J].中国卫生统计,2017,34(2):275-279.
[12] 张慧东,周连,陈晓东.淮安市空气PM2.5污染对呼吸系统疾病日门诊量影响[J].江苏预防医学,2015,26(6):41-43.
[13] 关绮华,梁自勉,莫莹莹.2017年佛山市大气PM2.5污染对呼吸系统疾病门诊量影响的时间序列分析[J].华南预防医学,2018,44(5):406-410.
[14] 秦萌,高阳,顾品强,等.上海市奉贤区大气污染物对人群呼吸系统疾病的急性效应[J].环境与职业医学,2018,35(6):521-525.
[15] Gleason JA,Fagliano JA.Associations of daily pediatric asthma emergency department visits with air pollution in Newark,NJ:utilizing time-series and case-crossover study designs[J].J Asthma,2015,52(8):815-822.
[16] 康真,刘洋,于天一,等.2015-2016年哈尔滨市大气PM2.5污染与儿童门诊量关系的时间序列研究[J].中国卫生工程学,2018,17(4):481-484.